Private AI Agents
with GPU Capacity You Control
Run AI agents and GPU workloads inside your own environment. We help teams automate repeatable software and operations work while keeping code, documents, and logs under your control.
What We Provide
We help teams stand up private agent capacity and GPU infrastructure without forcing sensitive work into a public SaaS workflow.
Private AI Agents
Deploy agents in your own environment for code maintenance, data processing, admin workflows, and other repeatable work that should stay close to your systems.
Learn more →GPU Compute
Plan and operate GPU capacity for agent runs, inference, fine tuning, batch jobs, and private model workloads.
Learn more →Workflow Learning
Document how teams work across tools, then turn repeatable steps into controlled agent workflows with review points and audit trails.
Learn more →Private AI Agents
Build agent capacity around the way your teams already work.
We help deploy an agent layer in your private cloud, VPC, or on premise environment. The goal is practical: identify repeatable work, define clear approval steps, and let agents handle tasks that do not need a person doing them by hand every time.
- Developer workflow mapping: Capture common repository tasks, test routines, release steps, and review patterns.
- Operations workflow mapping: Turn recurring spreadsheet, document, CRM, and reporting work into structured agent tasks.
- Review before execution: Keep human approval where it matters, especially around production systems and sensitive data.
- Private coding pipelines: Use coding agents for bug fixes, style cleanup, tests, and migration work inside your own perimeter.
- Local control: Keep workflow data, agent runners, and logs within the infrastructure you choose.
GPU Compute
Capacity planning and infrastructure support for private model workloads.
Agent systems need predictable compute. We help size, source, and operate GPU environments for inference, fine tuning, batch processing, and internal automation.
Agent Workloads
Run multiple agent jobs in parallel with clearer control over queues, limits, and workload priority.
Model Inference and Fine Tuning
Operate open source or custom models close to your data, with deployment choices that match your security rules.
Batch Processing
Process documents, embeddings, logs, and data cleanup jobs on schedules that fit your operations.
Vision and Multimodal Tasks
Support image, document, code, and schema workflows where GPU capacity and data controls both matter.
Keep Code and Data in Your Environment
Deploy in your private cloud, on premise servers, or isolated enterprise infrastructure. Source code, logs, documents, and sensitive business data remain within the environment you approve.
Private Cloud Deployment
Deploy within your own cloud account and keep workloads inside your network boundaries.
On Premise Deployment
Run on your own servers or isolated internal systems when workloads need tighter control.
Hybrid Deployment
Keep sensitive workloads local while adding managed compute capacity where it makes sense.
Access Control
Connect to SSO and role based access controls so agents only reach the systems they need.
Audit Logs
Track prompts, code changes, approvals, and system actions for review and compliance.
Repository Integration
Connect to repositories through approved keys, internal proxies, and existing review workflows.
CI/CD Integration
Send lint, build, and test results back to agents inside your development environment.
Data Boundary Protection
Use network rules and logging policies to reduce unapproved outbound data paths.
Model Training Controls
Keep code, prompts, and metadata out of public training pipelines unless you explicitly approve another path.
Common Workloads
These are the areas where private agents and GPU capacity usually create the clearest operational value.
Repository Maintenance
Handle recurring code cleanup, dependency updates, test fixes, and repository setup work.
Office Workflow Support
Turn repeatable spreadsheet, form, and document tasks into reviewed agent workflows.
Bulk Data Entry
Support invoice processing, customer data migration, and record updates across internal systems.
Bug Fixing
Use agents to inspect logs, propose patches, and run tests before changes go to review.
Test Generation
Identify missing coverage and draft unit or integration tests for engineering review.
Legacy System Migration
Break older code migration work into smaller batches that can be tested, reviewed, and tracked.
Code Review and Security Checks
Run pre merge checks, review common findings, and prepare fixes for static analysis issues.
Documentation Updates
Refresh API notes, architecture summaries, and function level documentation as code changes.
Model Inference
Run private inference workloads with capacity planning around latency, cost, and data location.
Batch Processing
Process documents, logs, embeddings, and internal datasets through scheduled GPU jobs.
Private Model Fine Tuning
Adapt internal helper models to your codebase, terminology, and review standards.
Deployment Options
Choose the setup that fits your security model, team structure, and infrastructure preferences.
Private Cloud
Deploy into your own cloud account and connect to existing network controls.
Private VPC NodesOn Premise
Run inside your own servers, private data centers, or local virtualization environment.
Bare Metal or Private SANHybrid
Keep the agent layer close to sensitive systems while adding managed compute capacity as needed.
Federated ExecutionDiscuss Your Deployment
Tell us what you are trying to deploy, where it needs to run, and what constraints matter. We will follow up by email.
We do not offer a public trial. Pilot work starts after we understand your security and deployment requirements.